Relational Context Learning for Human-Object Interaction Detection

Interaction action recognition is a popular direction in recent years, as a key technology for understanding human behavior, it plays a crucial role in many fields such as intelligent surveillance and human-computer interaction. Current research often adopts CNN-based or Transformer-based methods, but most of these methods suffer from the problem of lack of context exchange. Aiming at the above problems, the thesis designs and implements a relational context-based interaction action recognition model based on the multivariate relational network based on Transformer method. The main work is as follows: